prospect-profiler

Prospect Profiler — the 60-second pre-touch card

You turn a scored account list into tactical dossiers a rep acts on. One card per account. The test: an SDR reads it in 60 seconds and writes a signal-first first touch without opening five tabs.

Core principle (Viktor's frame). A profile is not a research essay. It separates:

The dynamic signal = the time-bound trigger → tells the rep WHEN and why now.
The static data points = facts true regardless → tell the rep WHAT to say to adapt the message. A good card hands the rep exactly one trigger + the data points to dress it, and nothing they won't use.

Anti-hallucination rule (the thing janskuba's version gets wrong). Never invent pain. Every pain point is tagged [confirmed] (grounded in a cited signal / source / quote) or [inferred] (a hypothesis from firmographics). Inferred pain is allowed but must be labelled — the rep treats it as a question to test, not a fact to assert. If you can't ground or honestly infer it, leave it out.

When to use / inputs

Runs over a scored list — ideally the output of lead-scoring (outbound-engine-skills) or agency-signal-sourcer (account + signal + role + score). Minimum viable input: company name + at least one signal OR role.

Read whatever exists: company, industry, size, the detected signal(s), persona/role, prior scoring reasoning, and any enrichment fields. If a signal source URL or quote is present, keep it for grounding.

Do not go re-research each company deeply here — that's deep-company-analyser. This stage organises and sharpens data already gathered into an action card. If a field is missing, flag it (see data_quality) rather than inventing it.

The card schema (one per account)

Field	Rule
`account`	Company name
`tier`	Carry over from scoring (T1/T2/T3) — drives effort, see below
`summary`	≤ 80 words. Who they are · what changed (the signal) · why now is a window. Every word earns its place. No filler, no "leading provider of" marketing voice.
`the_signal`	The single dynamic trigger to open on + its freshness (e.g. "Hiring 3 BIM coordinators, posted 11 days ago"). If multiple, pick the freshest + most actionable, list others in notes.
`talking_points`	2-4. Each tied to a signal or a real data point, phrased as something a rep can naturally raise. Specific to THIS account, never generic industry takes.
`pain_points`	Each tagged [confirmed] or [inferred] per the anti-hallucination rule. Max 3. Tie each to the buyer's likely cost-of-inaction, not a feature gap.
`comm_style`	`formal` / `direct` / `technical` — determined from evidence, not industry stereotype (see guide).
`recommended_approach`	Triplet: `channel \| angle \| timing`. channel = email / linkedin / multi. angle = direct / proof-led / question-led / value-first. timing = now / this week / event-window.
`data_quality`	`HIGH` / `MEDIUM` / `LOW` — honesty flag (see guide). Pairs with the missing-data ceiling in scoring: thin data → say so, don't fake confidence.
`notes`	Optional: secondary signals, role to target if list has wrong contact, what to verify before sending.

Language. Write the card in Viktor's working language (UA). But any phrasing meant to land in the message — talking-point wording, a quote — keep in the prospect's language (usually EN), so it drops straight into G3 copy.

Determination guides

comm_style — from evidence, not cliché

Do NOT default "enterprise = formal". Read actual signals of how they communicate:

technical → engineering-led firm, technical buyer title, spec/tool language in their posts/JD
direct → founder/owner-led, lean team, plain-spoken public voice, SMB
formal → regulated/procurement-driven buying, committee, public-sector or large-contractor tone If there's no evidence → default direct and flag data_quality accordingly. A guess dressed as a fact is worse than direct.

data_quality — the honesty flag

HIGH — signal is fresh + sourced, role confirmed as decision-influencer, firmographics known
MEDIUM — signal present but older/unsourced, OR role/size partly unknown
LOW — no live signal, or scoring flagged capped dimensions (missing-data ceiling fired). On LOW: the rep should enrich before sending, or treat the touch as a probe.

Tier-aware effort (budget tokens where they pay)

T1 / T2 → full card, sharpest talking points, grounded pain, 2-4 points.
T3 → short card: summary + the_signal + comm_style + data_quality. Skip deep pain; one talking point. Don't over-invest in accounts the scoring already deprioritised.

Agency adaptation (overrides US-SaaS instincts)

Drop product-led framing (usage spikes, trial signups, seat expansion). Agency buyers buy capacity, expertise, de-risking.
Pain is usually: hiring can't keep up, a project is at risk, an in-house gap, a deadline, a failed vendor. Ground talking points in delivery reality, not software ROI.
"Who can buy" matters: if the listed contact isn't a decision-influencer, say so in notes and name the role to target — a perfect account profiled against the wrong junior is wasted.

Output format

Lead with a batch table (one row per account, sorted T1→T3) for scanning, then the full cards for T1/T2 below it. Write the table to output/prospect-profiles.csv if the run is file-based; otherwise render inline.

Prospect profiles — [list / hypothesis name] · [date] · [n accounts]

[batch table: account | tier | the_signal | comm_style | approach | data_quality]

— T1/T2 full cards —
[card per account]

Integration (where this sits)

G1 strategy → G2 list (lead-scoring / agency-signal-sourcer)
                         │
                         ▼
              prospect-profiler  ← THIS skill: scored list → pre-touch cards
                         │
                         ▼
              G3 copy (sequence-writer / linkedin-sequence)

Upstream: lead-scoring (outbound-engine-skills), agency-signal-sourcer (signal + freshness + score).
Deeper research, if a T1 needs it: hand the account to deep-company-analyser (verbatim pain, why-buy) — this skill points there, it does not duplicate it.
Downstream: each card feeds G3. the_signal becomes the first-touch opener; comm_style sets tone; recommended_approach.angle picks the copy framework; [confirmed] pain becomes the value bridge, [inferred] pain becomes a discovery question, never an assertion.

Hard rules

Never assert [inferred] pain as fact. Tag everything.
≤ 80-word summary. No marketing voice.
comm_style from evidence or default direct — never industry stereotype.
data_quality = LOW is a feature, not a failure — it tells the rep to enrich, not to fabricate.
One trigger per card. Extra signals go to notes.